AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks

• URL: http://arxiv.org/abs/2201.09390v1
• Date: Sun, 23 Jan 2022 22:48:36 GMT
• Title: AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
• Authors: Dmitrijs Kass and Ekta Vats
• Abstract summary: This work proposes an attention-based sequence-to-sequence model for handwritten word recognition. It exploits models pre-trained on scene text images as a starting point towards tailoring the handwriting recognition models. The effectiveness of the proposed end-to-end HTR system has been empirically evaluated on a novel multi-writer dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems. To overcome training data scarcity, this work leverages models pre-trained on scene text images as a starting point towards tailoring the handwriting recognition models. ResNet feature extraction and bidirectional LSTM-based sequence modeling stages together form an encoder. The prediction stage consists of a decoder and a content-based attention mechanism. The effectiveness of the proposed end-to-end HTR system has been empirically evaluated on a novel multi-writer dataset Imgur5K and the IAM dataset. The experimental results evaluate the performance of the HTR framework, further supported by an in-depth analysis of the error cases. Source code and pre-trained models are available at https://github.com/dmitrijsk/AttentionHTR.

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